An Appeal to Reason: A Review of Roy B. Flemming, Tournament of Appeals: Granting Judicial Review in Canada
Bibliographic record
Abstract
The Supreme Court of Canada has great discretion in control over the contents of its docket. Annually, it receives somewhere between 500 and 600 leave to appeal applications and generally decides to hear somewhere around 100 of those cases. Why do some appeals reach the Court while others do not? In his slim but stimulating study, Flemming addresses the leave process as a prize to be sought - how does the Supreme Court of Canada set its agenda through deciding on the winners in this and what are the implications of that agenda-setting? As with many areas of judicial process, this question occupies a voluminous American literature, with which Flemming is well versed, and a sparse Canadian literature to which Flemming makes an important and worthwhile, if somewhat incomplete, contribution.This review is divided into three sections. The first section explores Flemming's claim that the Court deploys the leave process as a means of agenda-setting. The second section examines the tournament metaphor and the meaning Flemming attributes to it from his comparative perspective. Underlying the claims explored in the first two sections of this review is the assumption that we must examine motivations for deciding leave to appeal applications in indirect ways, because the Supreme Court does not provide direct justifications of its leave decisions through reasons. Finally, in the third section, I question this assumption and argue for revisiting the Court's rationale for exercising this critical discretion without justification.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.014 | 0.024 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".